def train_model(args):
trace('making vocaburaries ...')
src_vocab = Vocabulary.new(gens.word_list(args.source), args.vocab)
trg_vocab = Vocabulary.new(gens.word_list(args.target), args.vocab)
trace('making model ...')
model = AttentionalTranslationModel.new(src_vocab, trg_vocab, args.embed, args.hidden)
for epoch in range(args.epoch):
trace('epoch %d/%d: ' % (epoch + 1, args.epoch))
trained = 0
gen1 = gens.word_list(args.source)
gen2 = gens.word_list(args.target)
gen3 = gens.batch(gens.sorted_parallel(gen1, gen2, 100 * args.minibatch, order=0), args.minibatch)
model.init_optimizer()
for src_batch, trg_batch in gen3:
src_batch = fill_batch2(src_batch)
trg_batch = fill_batch2(trg_batch)
K = len(src_batch)
hyp_batch = model.train(src_batch, trg_batch)
for k in range(K):
trace('epoch %3d/%3d, sample %8d' % (epoch + 1, args.epoch, trained + k + 1))
trace(' src = ' + ' '.join([x if x != '</s>' else '*' for x in src_batch[k]]))
trace(' trg = ' + ' '.join([x if x != '</s>' else '*' for x in trg_batch[k]]))
trace(' hyp = ' + ' '.join([x if x != '</s>' else '*' for x in hyp_batch[k]]))
trained += K
trace('saving model ...')
model.save(args.model + '.%03d' % (epoch + 1))
trace('finished.')
def train_model(args):
trace('making vocabularies ...')
src_vocab = Vocabulary.new(gens.word_list(args.source), args.vocab)
trg_vocab = Vocabulary.new(gens.word_list(args.target), args.vocab)
trace('making model ...')
model = AttentionalTranslationModel.new(src_vocab, trg_vocab, args.embed,
args.hidden)
for epoch in range(args.epoch):
trace('epoch %d/%d: ' % (epoch + 1, args.epoch))
trained = 0
gen1 = gens.word_list(args.source)
gen2 = gens.word_list(args.target)
gen3 = gens.batch(
gens.sorted_parallel(gen1, gen2, 100 * args.minibatch, order=0),
args.minibatch)
model.init_optimizer()
for src_batch, trg_batch in gen3:
src_batch = fill_batch2(src_batch)
trg_batch = fill_batch2(trg_batch)
K = len(src_batch)
hyp_batch = model.train(src_batch, trg_batch)
for k in range(K):
trace('epoch %3d/%3d, sample %8d' %
(epoch + 1, args.epoch, trained + k + 1))
trace(
' src = ' +
' '.join([x if x != '</s>' else '*'
for x in src_batch[k]]))
trace(
' trg = ' +
' '.join([x if x != '</s>' else '*'
for x in trg_batch[k]]))
trace(
' hyp = ' +
' '.join([x if x != '</s>' else '*'
for x in hyp_batch[k]]))
trained += K
trace('saving model ...')
model.save(args.model + '.%03d' % (epoch + 1))
trace('finished.')
def test_model(args):
trace('loading model ...')
model = AttentionalTranslationModel.load(args.model)
trace('generating translation ...')
generated = 0
with open(args.target, 'w') as fp:
for src_batch in gens.batch(gens.word_list(args.source), args.minibatch):
src_batch = fill_batch2(src_batch)
K = len(src_batch)
trace('sample %8d - %8d ...' % (generated + 1, generated + K))
hyp_batch = model.predict(src_batch, args.generation_limit)
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
six.print_(' '.join(hyp), file=fp)
generated += K
trace('finished.')
def test_model(args):
trace('loading model ...')
model = AttentionalTranslationModel.load(args.model)
trace('generating translation ...')
generated = 0
with open(args.target, 'w') as fp:
for src_batch in gens.batch(gens.word_list(args.source),
args.minibatch):
src_batch = fill_batch2(src_batch)
K = len(src_batch)
trace('sample %8d - %8d ...' % (generated + 1, generated + K))
hyp_batch = model.predict(src_batch, args.generation_limit)
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print(' '.join(hyp), file=fp)
generated += K
trace('finished.')